Career Readiness in Young Adult Brain Tumor Survivors

David R. Strauser, Ph.D.Department of Kinesiology and Community

Health October 19, 20091:00-2:30 p.m.

Overview

Overview of the problem

Overview of Cognitive Information Processing

Brief Review of foundational research

Initial results of current young adult career readiness study findings

Applications to service delivery

The Broad Problem

As the number of individuals surviving cancer continues to increase, career developmentcareer development and employmentemployment are becoming central factors that impact the individual’s community integration community integration and well- well-beingbeing.

Cancer and Employment: AdultsCancer and Employment: Adults

Employment rates of cancer survivors range from 41 to 84%

– Mean rate of 62% (Taskilia &Lindbohm, 2007)

– 16.8% of working age cancer survivors (vs. 5% for matched controls) are “unable to work because of physical, mental, or emotional problems”

7.4% (vs. 3.2% of matched controls) “were limited in the kind or amount of work they could perform” (Hewitt, Rowland, & Yancik, 2003)

Long term cancer survivors

One in five survivors reported “cancer related disabilities” with 50% continuing to work.

13% of all survivors had withdrawn from work for cancer related reasons within four years (Short et al, 2005)

Cancer and Employment: AdultCancer and Employment: Adult

Unequal employment practices are forms of discrimination for cancer survivors (Feuerstein, et al., 2007)– More likely to file claims related to job loss and differential

treatment– Cancer combined with another impairment increases

relationship problems at work

Duration of sick leave associated with difficulties in returning to work in cancer survivors (Amir et al. 2007) – Males use less sick leave then females

– Males have longer sick leave

– Sick leave used most by those economically deprived

Cancer and Employment: Young Cancer and Employment: Young AdultsAdults

Young adults who are childhood cancer survivors experience significant difficulty in obtaining employment– Adult survivors of childhood cancer are twice as likely to be

unemployed compared to their healthy controls – 5 times more likely to be unemployed in CNS cancer survivors.

(deBoer et al, 2006)

Young adults who are childhood cancer survivors experience concerns about obtaining employment– 19% with a history of childhood bone marrow transplant reported

work as a major concern vs. 2% of their age matched controls (Bradley,2002)

– Co-Morbid health conditions (depression, anxiety, fatigue, cardiovascular disease, visual impairments, and impaired attention span) have been linked to difficulty in obtaining employment in childhood cancer survivors (deBoer et al, 2006)

Career Development and Cancer Survivorship

Career counseling has an important role with young cancer survivors

Cognitive Information Processing (CIP) provides a theoretical model regarding the provision of career services

– Two core constructs Information Processing Domains

Decision-Making Cycle

Theoretical Assumptions (CIP)

Career decision making involves both emotions (affect) and thoughts (cognition)– Knowledge-content of career choice– Process-what we need to do

Knowledge and Emotions are dynamic states– Impacted by health and disability (i.e. cancer)

Career problem solving and decision making are skills that can be learned– Can improve with practice– Career counseling and resources

Dysfunctional Career Thoughts

Dysfunctional career thoughts lead to…– Avoidance of career and other life decisions– Decreased life satisfaction– Depression and anxiety– Decreased job satisfaction– Increased job stress– Interpersonal relationship problems

Expressed through…– Behavior-poor performance– Verbal-negative statements and expressions– Emotions-depression and anxiety

Information Processing DomainsInformation Processing Domains

Executive Processing Domain

Knowledge Domains

Decision-Making Skills Domain

SelfKnowledge

Environmental Knowledge

CASVE Cycle

Meta-cognitions

Self-Knowledge Values, interests, skills, and employment preferences

are influenced by– Personal characteristics– Life experience

Values, interests, skills, and employment preferences may be influenced by – Religious or spiritual beliefs

Cancer Related Issues– Symptom burden-fatigue, depression, anxiety, decreased

concentration

Receptive and Expressive skills

Self-Knowledge Stored in episodic memory

Perceptions rather than facts

Influenced by interpretation of past events

Influenced by present emotions

Environmental Knowledge Knowledge of Environmental Demands

– Direct experience or observing others

– Expands over time

Components of Environmental Knowledge– Tasks

– Tools & Technology

– Knowledge (i.e. language, interpersonal)

– Skills & Abilities

– Work Activities (i.e. communicating, moving objects)

– Work Context ( i.e. contact with others)

– Work Styles (i.e. stress tolerance, dependability)

– Work Values (i.e. relationship, support, achievement)

– Interests

Expressive and Receptive demands

Occupational Knowledge

Stored in semantic memory

Verifiable facts rather than perceptions

Not influenced by interpretation of past events

Not influenced by present emotions

Decision-Making Cycle

Communication

AnalysisExecution

SynthesisValuing

Meta-Cognitions (Executive Processing Domain)

Thoughts That Influence Decision Making– Self-TalkSelf-Talk

Positive vs. Negative– Self-AwarenessSelf-Awareness

Thoughts, Emotions and Behaviors Reactions of self to significant others

– Monitoring and ControlMonitoring and Control Where they are in the process Purposeful engagement

Two Dimensions of Career Readiness

Capability– Cognitive and affective ability to engage in effective

career problem solving and decision making

Career Thoughts Inventory (CTI) Subscales– Decision Making Confusion (DMC)– Commitment Anxiety (CA)

Complexity– Contextual factors, originating in the family, society,

economy, or employing organizations that make it (more or less) difficult to solve career problems or make decisions CTI Subscale

– External Conflict (EC)

Complexity(High)

Low ReadinessHigh Degree of Support Needed

(Individual Case Managed (Individual Case Managed Services)Services)

Capability

Moderate ReadinessModerate to Low Support Needed

(Brief Staff Assisted Services)(Brief Staff Assisted Services)

Capability

(Low)

Moderate Degree of ReadinessModerate Degree of Support

Needed

(Brief Staff-Assisted Services)(Brief Staff-Assisted Services)

(High)

High ReadinessNo Support Needed

(Self-Help Services)(Self-Help Services)

Complexity(Low)

Two Dimensional Model of Readiness

Foundational Research

Career Readiness and Disability

Key Findings (Disability Specific)

People with disabilities have increased levels of dysfunctional career thoughts compared to controls (Strauser et al, 2002, Strauser et al., 2004)

The Career Thoughts Inventory (CTI) can be used with individuals with disabilities to identify their levels of career readiness (Lustig et al., 2003)

Cognitive and affective states negatively impact career readiness (Yanchak et al., 2005; Strauser et al., 2006a; Strauser et al., 2006b)

PWB is positively related to career readiness (Lustig et al, 2002; Lustig et al. 2008; Strauser et al, 2008)

Key Findings (Broader Career Literature)

Career services are effective in reducing negative career thoughts (Dipeolu et al. 2002)

Completion of higher education is related to less dysfunctional career thoughts (Reardon, et al., 2000)

Positive effect of Vocational and Career services for individuals with disabilities (Bolton & Akridge, 1995; Enright, 1995; Merz & Syzmanski, 2002)

Current Research in Career

Readiness and Young Adult CNS

Survivors

Purpose of Current Exploratory Study

The overall purpose of this exploratory study is two fold.

– Examine the relationship between career readiness, vocational identify and relevant career and psycho-social outcomes in young adult brain tumor survivors

Enhancing career readiness can increase career and vocational functioning and career and psycho-social outcomes

– Determine if we can classify brain tumor survivors according to their level of career readiness

Classifying survivors according to their level of career readiness can guide the implementation of clinical and vocational interventions focused on improving career and employment outcomes.

Aim 1

Aim 1– Examine the relationship between career readiness,

vocational identity, and relevant work and psycho-social outcomes

Ho: There will be a significant and positive relationship between career readiness, vocational identify, and relevant work and psycho-social outcomes

Complexity

Capability

Identity Community Integration

Employment Outcomes

Individual Well-Being

Conceptual Model Guiding Aim 1

Aim 2

Aim 2– Determine if it is feasible to classify brain tumor

survivors according to their reported level of career readiness.

Ho: Brain tumor survivors can be classified according their reported level of career readiness. Specifically, we hypothesize that we will be able to classify individuals into three groups (High, Moderate, and Low) which then can be used to guide the type, level, and implementation of career intervention

Complexity(High)

Low ReadinessHigh Degree of Support Needed

(Individual Case Managed (Individual Case Managed Services)Services)

Capability

Moderate ReadinessModerate to Low Support Needed

(Brief Staff Assisted Services)(Brief Staff Assisted Services)

Capability

(Low)

Moderate Degree of ReadinessModerate Degree of Support

Needed

(Brief Staff-Assisted Services)(Brief Staff-Assisted Services)

(High)

High ReadinessNo Support Needed

(Self-Help Services)(Self-Help Services)

Complexity(Low)

Intervention Matrix for Aim 2

Procedures

Surveys distributed to young adult brain tumor survivors

Age 18-30 IRB approval Research packet contained

– Demographic Form– Career Thoughts Inventory (CTI)– Contextual Work Behaviors (CWB)– Community Integration Scale (CIS)– Satisfaction with Life Scale (SWLS)

Participating Sites

Children’s Brain Tumor Foundation

St. Jude Children’s Hospital

Camp-Make-A-Dream

Hope Advocate Hospital-Chicago

L.A. Children’s Hospital-City of Hope

Demographic Characteristics of Participants (N=37). Total (N = 37)

Gender  

Male 45.9%

Female 54.1%

Ethnicity

Caucasian 76.5%

African American 8.8%

Hispanic 5.9%

Asian/ Pacific Islander 8.8%

Education

Grade 12 or below 27.0%

Community college 24.3%

Some college 35.1%

4-year college 10.8%

Graduate school 2.7%

Mean Age (years) 21.9 (SD = 3.3)

Mean age of Onset of Brain Tumor (years) 9.9 (SD = 5.5)

Mean years off Treatment (years) 7.0 (SD = 6.0)

Study Demographics

Multiple linear regression (Stepwise) for total score and 3 subscale scores of Career Thoughts Inventory (CTI), predicting total score of Vocational Identity (MVS) (N= 37)

Variables in the equation

Variables B SEB β t p

CTI (Total) -.151 .020 -.790 -7.623 <.001

CTI (DMC) -.152 -.564 .576

CTI (CA) -.164 -.652 .519

CTI (EC) -.088 -.599 .553

Constant 17.385 1.085 16.018 <.001

Note: R2=.61, Dependent variables: Total score (Vocational Identity) of the My Vocational Situation; Predictor variables: CTI (Total) = Total score; CTI (DMC) = Decision Making Confusion; CTI (CA) = Commitment Anxiety; CTI (EC) = External Conflict.

Results Aim 1

When the four independent variables are entered using stepwise method, the total score of CTI alone significantly predicted the total score (Vocational Identity) of MVS, F= 58.110, p < .001.

The adjusted R squared value was 0.613, indicating that 61.3% of the variance in the total score (Vocational Identity) of MVS was explained by this model.

CTI Subscales (DMC, CA, and EC) did not make a unique contribution to the model

Aim 1 Results

Multivariate analysis procedure for Vocational Identity total score predicting the total scores of Contextual Work Behaviors (CWB), Community Integration Scale (CIS), and Satisfaction with Life Scale (SWLS) (N=37).

Variables MS F p Adjusted R2 Power

CWB (Total) 10919.386 9.293 .004 .186 .842

CIS (Total) 227.081 5.264 .028 .106 .607

SWLS (Total) 207.873 4.201 .048 .082 .513

Note: Dependent variables: CWB (Total) = total score of Contextual Work Behavior (CWB); CIS (Total) = total score of Community Integration Scale (CIS); SWLS (Total) = total score of Satisfaction with Life Scale (SWLS). Independent variable: VI (Total) = total score (Vocational Identity) of the My Vocational Situation (MVS).

Multivariate Results Research Aim 1

Result indicated that the overall model was significant (Pillai’s Trace F (3, 33) = 4.131, p = .014)

Vocational Identify total score significantly predicted:

CWB (R2 value = .186) 18.6% of the Variance

CIS (R2 value = .106) 10.6% of the Variance

SWLS (R2 value = .082) 8.2% of the Variance

Multivariate Results Aim 1

Summary Aim 1 Findings

Career Thoughts significantly and positively related to vocational identity

Complexity and capability not a unique predictor at this time

Vocational identity significantly and positively related to:– Contextual Work Behaviors (CWB)– Community Integration (CIS)– Individual Well-being (SWLS)

Grouping Results Aim 2

Cluster analysis (Ward’s method) was used to classify participants into three groups.

Results from Chi-square or F-statistics analysis indicated that all demographic characteristics, except were not significant (p > 0.05).

Readiness level Group 1(n=15)Moderate

Group 2(n=15)High

Group 3 (n=7)Low

Χ or F

Gender

Male 60.0% 33.3% 42.9% 2.181

Female 40.0% 66.7% 57.1% (p=.336)

Ethnicity

African American 7.1% 7.1% 16.7% 11.603

Caucasian 78.6% 85.7% 50.0% (p=.071)

Hispanic 0.0% 0.0% 33.3%

Asian/ Pacific Islander 14.3% 7.1% 0.0%

Table 4. Demographic Characteristics of Participants in Four Cluster Groups (N=37).

Grouping Results Aim 2

Readiness level Group 1(n=15)Moderate

Group 2(n=15)High

Group 3 (n=7)Low

Χ or F

Education

Grade 12 or below 20.0% 26.7% 42.9% 3.726

Community college 20.0% 26.7% 28.6% (p=.881)

Some college 40.0% 33.3% 28.6%

4-year college 13.3% 13.3% 0.0%

Graduate school 6.7% 0.0% 0.0%

Mean Age (years) 22.2 (SD = 2.7) 22.3 (SD = 4.1) 20.3 (SD = 2.6) .973

(p=.389)

Mean age of Onset of Brain Tumor (years)

10.2 (SD = 6.1) 10.0 (SD = 5.3) 9.4 (SD = 5.4) .045(p=.956)

Mean years off Treatment (years)

8.3 (SD = 4.0) 7.4 (SD = 7.9) 3.8 (SD = 3.3) 1.141(p = .334)

Note: Participants were clustered by the total score and three subscale scores (DMC, CA, & EA) of Career Thoughts Inventory (CTI)

Grouping Results Aim 2

The GLM Multivariate procedure was computed by entering the raw total score, and three raw subscale scores of CTI as dependent variables and 3-cluster solution as fixed variable.

Result indicated that the overall model was significant (Pillai’s Trace F (8, 64) = 6.410, p < .001).

Test of between-subjects effects further indicated that the CTI Total, DMC, CA, and EC are all significant [F (2. 34) = 8.356- 98.478, ps < 0.001].

Post-hoc analysis indicated that all pair-wise comparisons of all four variables across all three readiness groups are significantly different (ps < .05)

Except that there is no significant difference on EC score was found between Moderate and Low readiness groups (p = .478).

Generally speaking, the High readiness group tends to have lower negative career thoughts

lower DMC,CA, and EC than the other groups.

Results Aim 2

Figure 1. T-scores of the total score (CTI Total), and 3 subscale scores (Decision Making Confusion (DMC), Commitment Anxiety (CA), and External Conflict (EC) of Career Thoughts Inventory (CTI) across 3 readiness groups.

Note: T-score conversion was based on the profile for adults as reference. Lower T-score reflected less negative career thoughts, less DMC, less CA, & less EC. In other words, lower T-score reflected better readiness.

Differences on CTI across 3 Groups

Complexity

Capability

Identity Community Integration

Employment Outcomes

Individual Well-Being

Conceptual Model

Mean total score of Contextual Work Behavior (CWB Total) across 3 readiness groups.

Readiness level Group 1(n=15)Moderate

Group 2(n=15)High

Group 3 (n=7)Low

Overall (N=37)

CWB Total (Mean) 189.53 205.53 176.85 193.62

Readiness level Group 1(n=15)Moderate

Group 2(n=15)High

Group 3 (n=7)Low

Overall (N=37)

CIS Total (Mean) 21.33 17.87 23.00 20.24

Readiness level Group 1(n=15)Moderate

Group 2(n=15)High

Group 3 (n=7)Low

Overall (N=37)

SWLS Total (Mean) 18.87 22.87 22.14 21.11

Note: Participants were clustered by the total score and three subscale scores (DMC, CA, & EA) of Career Thoughts Inventory (CTI)

Mean scores of total score of Contextual Work Behavior (CWB) across 3 readiness groups.

Mean scores of total score of Community Integration Scale, across 3 readiness groups.

Mean scores of total score of Life Scale across 3 readiness groups.

Results Aim 2

Able to group cancer survivors according to their level of career readiness

No differences in demographic variables– Ethnicity-may emerge as significant (Low Group)– Education-may emerge as significant (Low Group)

No difference across groups for– CWB-may emerge as significant– CIS-may emerge as significant– SWLS

Implications for Practice

Readiness assessment valuable as a means to identify level of career and vocational intervention

– High Readiness- Self-help Services

– Moderate Readiness- Brief Assisted Services

– Low Readiness- Intensive Services

Implications

Improving Readiness may increase CWB– Job Maintenance behaviors

Handling stress Making adjustments Dealing with co-workers Dealing with supervisors

Improving Readiness may increase Community Integration

Level of community support Level of independence Level of occupational participation

Questions?Comments ?

Research Team